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pyxll/pyxll-agent-skills

A curated collection of Agent Skills for working with PYXLL, to help AI agents write and understand code using the PyXLL Excel add-in.

99

1.56x
Quality

90%

Does it follow best practices?

Impact

100%

1.56x

Average score across 17 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

Overview
Quality
Evals
Security
Files

task.mdevals/scenario-12/

Runtime-Populated Pricing Model Menu

Problem Description

A trading operations team uses a PyXLL Excel add-in. Quants regularly deploy new pricing model Python modules to the server, so the list of available models changes over time. Hard-coding a fixed ribbon menu would go stale within days.

The team lead wants a ribbon button that opens a menu populated at runtime: each time a user clicks the arrow, Python is called to determine which models are currently available and the menu is built on the fly. The ribbon module is pricing_ribbon.

Output Specification

Produce two files:

  • pricing_ribbon.xml — ribbon XML with a "Pricing" tab containing a menu button that is populated at runtime. The menu should refresh its contents each time it is opened (in case new models have been deployed since the last open).

  • pricing_ribbon.py — Python module containing all callback functions referenced from the XML. For the demo implementation, return a menu with at least two example pricing model buttons (e.g. Black-Scholes and Monte Carlo). Each button's onAction should also be implemented as a stub function.

README.md

tile.json